Radiative decays of dynamically generated charmed baryons

In this work we study the radiative decay of dynamically generated J^P=\oh^- charm baryons into the ground state J^P=\oh^+ baryons. Since different theoretical interpretations of these baryonic resonances, and in particular of the $\Lambda_c(2595)$, give different predictions, a precise experimental measurement of these decays would be an important step for understanding their nature.Comment: 10 pages, 1 figur

Chiral approach to antikaon s- and p-wave interactions in dense nuclear matter

The properties of the antikaons in nuclear matter are investigated from a chiral unitary approach which incorporates the s- and p-waves of the ${\bar K}N$ interaction. To obtain the in-medium meson-baryon amplitudes we include, in a self-consistent way, Pauli blocking effects, meson self-energies corrected by nuclear short-range correlations and baryon binding potentials. We pay special attention to investigating the validity of the on-shell factorization, showing that it cannot be applied in the evaluation of the in-medium corrections to the p-wave amplitudes. In nuclear matter at saturation energy, the $\Lambda$ and $\Sigma$ develop an attractive potential of about -30 MeV, while the $\Sigma^*$ pole remains at the free space value although its width gets sensibly increased to about 80 MeV. The antikaon also develops a moderate attraction that does not support the existence of very deep and narrow bound states, confirming the findings of previous self-consistent calculations.Comment: 29 pages, 12 figures, published in Physical Review

Charm at FAIR

Charmed mesons in hot and dense matter are studied within a self-consistent coupled-channel approach for the experimental conditions of density and temperature expected at the CBM experiment at FAIR/GSI. The $D$ meson spectral function broadens with increasing density with an extended tail towards lower energies due to $\Lambda_c(2593) N^{-1}$ and $\Sigma_c(2800) N^{-1}$ excitations. The in-medium $\bar D$ meson mass increases with density. We also discuss the consequences for the renormalized properties in nuclear matter of the charm scalar $D_{s0}(2317)$ and D(2400), and the predicted hidden charm X(3700) resonances at FAIR energies.Comment: 6 pages, 3 figures, to appear in the proceedings of ExcitedQCD 09, Zakopane, Poland, 8-14 February 200

Noise characterization of an atomic magnetometer at sub-millihertz frequencies

Noise measurements have been carried out in the LISA bandwidth (0.1 mHz to 100 mHz) to characterize an all-optical atomic magnetometer based on nonlinear magneto-optical rotation. This was done in order to assess if the technology can be used for space missions with demanding low-frequency requirements like the LISA concept. Magnetometry for low-frequency applications is usually limited by $1/f$ noise and thermal drifts, which become the dominant contributions at sub-millihertz frequencies. Magnetic field measurements with atomic magnetometers are not immune to low-frequency fluctuations and significant excess noise may arise due to external elements, such as temperature fluctuations or intrinsic noise in the electronics. In addition, low-frequency drifts in the applied magnetic field have been identified in order to distinguish their noise contribution from that of the sensor. We have found the technology suitable for LISA in terms of sensitivity, although further work must be done to characterize the low-frequency noise in a miniaturized setup suitable for space missions.Comment: 11 pages, 12 figure

The Minimum Description Length Principle and Model Selection in Spectropolarimetry

It is shown that the two-part Minimum Description Length Principle can be used to discriminate among different models that can explain a given observed dataset. The description length is chosen to be the sum of the lengths of the message needed to encode the model plus the message needed to encode the data when the model is applied to the dataset. It is verified that the proposed principle can efficiently distinguish the model that correctly fits the observations while avoiding over-fitting. The capabilities of this criterion are shown in two simple problems for the analysis of observed spectropolarimetric signals. The first is the de-noising of observations with the aid of the PCA technique. The second is the selection of the optimal number of parameters in LTE inversions. We propose this criterion as a quantitative approach for distinguising the most plausible model among a set of proposed models. This quantity is very easy to implement as an additional output on the existing inversion codes.Comment: Accepted for publication in the Astrophysical Journa

Multi-Kˉ\bar{K} nuclei and kaon condensation

We extend previous relativistic mean-field (RMF) calculations of multi-$\bar K$ nuclei, using vector boson fields with SU(3) PPV coupling constants and scalar boson fields constrained phenomenologically. For a given core nucleus, the resulting $\bar K$ separation energy $B_{\bar K}$, as well as the associated nuclear and $\bar K$-meson densities, saturate with the number $\kappa$ of $\bar K$ mesons for $\kappa > \kappa_{\rm sat} \sim 10$. Saturation appears robust against a wide range of variations, including the RMF nuclear model used and the type of boson fields mediating the strong interactions. Because $B_{\bar K}$ generally does not exceed 200 MeV, it is argued that multi-$\bar K$ nuclei do not compete with multihyperonic nuclei in providing the ground state of strange hadronic configurations and that kaon condensation is unlikely to occur in strong-interaction self-bound strange hadronic matter. Last, we explore possibly self-bound strange systems made of neutrons and ${\bar K}^0$ mesons, or protons and $K^-$ mesons, and study their properties.Comment: 21 pages, 8 figures, revised text and reference

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18